import json
import copy
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
from torchsummary import summary
from nmfd_gnn import NMFD_GNN
print (torch.cuda.is_available())
device = torch.device("cuda:0")
random_seed = 42
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
r = random.random
True
#1.1: settings
M = 10 #number of time interval in a window
missing_ratio = 0.50
file_name = "m_" + str(M) + "_missing_" + str(int(missing_ratio*100))
print (file_name)
#1.2: hyperparameters
num_epochs, batch_size, learning_rate = 200, 16, 0.001
beta_flow, beta_occ, beta_phy = 1.0, 1.0, 0.1
batch_size_vt = 16 #batch size for evaluation and test
delta_ratio = 0.1 #the ratio of delta in the standard deviation of flow
hyper = {"n_e": num_epochs, "b_s": batch_size, "b_s_vt": batch_size_vt, "l_r": learning_rate,\
"beta_f": beta_flow, "beta_o": beta_occ, "beta_p": beta_phy, "delta_ratio": delta_ratio}
gnn_dim_1, gnn_dim_2, gnn_dim_3, lstm_dim = 2, 128, 128, 128
p_dim = 10 #column dimension of L1, L2
c_k = 5.5 #meter, the sum of loop width and uniform vehicle length. based on Gero and Daganzo 2008.
theta_ini = [-2.879, 5.207, -2.473, 1.722, 3.619]
hyper_model = {"g_dim_1": gnn_dim_1, "g_dim_2": gnn_dim_2, "g_dim_3": gnn_dim_3, "l_dim": lstm_dim,\
"p_dim": p_dim, "c_k": c_k, "theta_ini": theta_ini}
max_no_decrease = 30
#1.3: set paths
root_path = "/home/umni2/a/umnilab/users/xue120/umni4/2023_mfd_traffic_london/"
file_path = root_path + "2_prepare_data/" + file_name + "/"
train_path, vali_path, test_path =\
file_path + "train.json", file_path + "vali.json", file_path + "test.json"
sensor_id_path = file_path + "sensor_id_order.json"
sensor_adj_path = file_path + "sensor_adj.json"
mean_std_path = file_path + "mean_std.json"
m_10_missing_50
def visualize_train_loss(total_phy_flow_occ_loss):
plt.figure(figsize=(4,3), dpi=75)
t_p_f_o_l = np.array(total_phy_flow_occ_loss)
e_loss, p_loss, f_loss, o_loss = t_p_f_o_l[:,0], t_p_f_o_l[:,1], t_p_f_o_l[:,2], t_p_f_o_l[:,3]
x = range(len(e_loss))
plt.plot(x, p_loss, linewidth=1, label = "phy loss")
plt.plot(x, f_loss, linewidth=1, label = "flow loss")
plt.plot(x, o_loss, linewidth=1, label = "occ loss")
plt.legend()
plt.title('Loss decline on train')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.savefig(file_name + '/' + 'train_loss.png', bbox_inches = 'tight')
plt.show()
def visualize_flow_loss(vali_f_mae, test_f_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_f_mae))
plt.plot(x, vali_f_mae, linewidth=1, label="Validate")
plt.plot(x, test_f_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of flow on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE (veh/h)')
plt.savefig(file_name + '/' + 'flow_mae.png', bbox_inches = 'tight')
plt.show()
def visualize_occ_loss(vali_o_mae, test_o_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_o_mae))
plt.plot(x, vali_o_mae, linewidth=1, label="Validate")
plt.plot(x, test_o_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of occupancy on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE')
plt.savefig(file_name + '/' + 'occ_mae.png',bbox_inches = 'tight')
plt.show()
def MAELoss(yhat, y):
return float(torch.mean(torch.div(torch.abs(yhat-y), 1)))
def RMSELoss(yhat, y):
return float(torch.sqrt(torch.mean((yhat-y)**2)))
def vali_test(model, f, f_mask, o, o_mask, f_o_mean_std, b_s_vt):
flow_std, occ_std, n = f_o_mean_std[1], f_o_mean_std[3], len(f)
f_mae_list, f_rmse_list, o_mae_list, o_rmse_list, num_list = list(), list(), list(), list(), list()
for i in range(0, n, b_s_vt):
s, e = i, np.min([i+b_s_vt, n])
num_list.append(e-s)
bf, bo, bf_mask, bo_mask = f[s: e], o[s: e], f_mask[s: e], o_mask[s: e]
bf_hat, bo_hat, bq_hat, bq_theta = model.run(bf_mask, bo_mask)
bf_hat, bo_hat = bf_hat.cpu(), bo_hat.cpu()
bf_mae, bf_rmse = MAELoss(bf_hat, bf)*flow_std, RMSELoss(bf_hat, bf)*flow_std
bo_mae, bo_rmse = MAELoss(bo_hat, bo)*occ_std, RMSELoss(bo_hat, bo)*occ_std
f_mae_list.append(bf_mae)
f_rmse_list.append(bf_rmse)
o_mae_list.append(bo_mae)
o_rmse_list.append(bo_rmse)
f_mae, o_mae = np.dot(f_mae_list, num_list)/n, np.dot(o_mae_list, num_list)/n
f_rmse = np.sqrt(np.dot(np.multiply(f_rmse_list, f_rmse_list), num_list)/n)
o_rmse = np.sqrt(np.dot(np.multiply(o_rmse_list, o_rmse_list), num_list)/n)
return f_mae, f_rmse, o_mae, o_rmse
def evaluate(model, vt_f, vt_o, vt_f_m, vt_o_m, f_o_mean_std, b_s_vt): #vt: vali_test
vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse =\
vali_test(model, vt_f, vt_f_m, vt_o, vt_o_m, f_o_mean_std, b_s_vt)
return vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse
import torch
#4.1: one training epoch
def train_epoch(model, opt, criterion, train_f_x, train_f_y, train_o_x, train_o_y, hyper, flow_std_squ, delta):
#f: flow; o: occupancy
model.train()
losses, p_losses, f_losses, o_losses = list(), list(), list(), list()
beta_f, beta_o, beta_p, b_s = hyper["beta_f"], hyper["beta_o"], hyper["beta_p"], hyper["b_s"]
n = len(train_f_x)
print ("# batch: ", int(n/b_s))
for i in range(0, n-b_s, b_s):
time1 = time.time()
x_f_batch, y_f_batch = train_f_x[i: i+b_s], train_f_y[i: i+b_s]
x_o_batch, y_o_batch = train_o_x[i: i+b_s], train_o_y[i: i+b_s]
opt.zero_grad()
y_f_hat, y_o_hat, q_hat, q_theta = model.run(x_f_batch, x_o_batch)
#p_loss = criterion(q_hat, q_theta).cpu() #physical loss
#p_loss = p_loss/flow_std_squ
#hinge loss
q_gap = q_hat - q_theta
delta_gap = torch.ones(q_gap.shape, device=device)*delta
zero_gap = torch.zeros(q_gap.shape, device=device) #(n, m)
hl_loss = torch.max(q_gap-delta_gap, zero_gap) + torch.max(-delta_gap-q_gap, zero_gap)
hl_loss = hl_loss/flow_std_squ
p_loss = criterion(hl_loss, zero_gap).cpu() #(n, m)
f_loss = criterion(y_f_hat.cpu(), y_f_batch) #data loss of flow
o_loss = criterion(y_o_hat.cpu(), y_o_batch) #data loss of occupancy
loss = beta_f*f_loss + beta_o*o_loss + beta_p*p_loss
loss.backward()
opt.step()
losses.append(loss.data.numpy())
p_losses.append(p_loss.data.numpy())
f_losses.append(f_loss.data.numpy())
o_losses.append(o_loss.data.numpy())
if i % (64*b_s) == 0:
print ("i_batch: ", i/b_s)
print ("the loss for this batch: ", loss.data.numpy())
print ("flow loss", f_loss.data.numpy())
print ("occ loss", o_loss.data.numpy())
time2 = time.time()
print ("time for this batch", time2-time1)
print ("----------------------------------")
n_loss = float(len(losses)+0.000001)
aver_loss = sum(losses)/n_loss
aver_p_loss = sum(p_losses)/n_loss
aver_f_loss = sum(f_losses)/n_loss
aver_o_loss = sum(o_losses)/n_loss
return aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss
#4.2: all train epochs
def train_process(model, criterion, train, vali, test, hyper, f_o_mean_std):
total_phy_flow_occ_loss = list()
n_mse_flow_occ = 0 #mse(flow) + mse(occ) for validation sets.
f_std = f_o_mean_std[1]
vali_f, vali_o = vali["flow"], vali["occupancy"]
vali_f_m, vali_o_m = vali["flow_mask"].to(device), vali["occupancy_mask"].to(device)
test_f, test_o = test["flow"], test["occupancy"]
test_f_m, test_o_m = test["flow_mask"].to(device), test["occupancy_mask"].to(device)
l_r, n_e = hyper["l_r"], hyper["n_e"]
opt = optim.Adam(model.parameters(), l_r, betas = (0.9,0.999), weight_decay=0.0001)
opt_scheduler = torch.optim.lr_scheduler.MultiStepLR(opt, milestones=[150])
print ("# epochs ", n_e)
r_vali_f_mae, r_vali_o_mae, r_test_f_mae, r_test_o_mae = list(), list(), list(), list()
r_vali_f_rmse, r_vali_o_rmse, r_test_f_rmse, r_test_o_rmse = list(), list(), list(), list()
flow_std_squ = np.power(f_std, 2)
no_decrease = 0
for i in range(n_e):
print ("----------------an epoch starts-------------------")
#time1_s = time.time()
time_s = time.time()
print ("i_epoch: ", i)
n_train = len(train["flow"])
number_list = copy.copy(list(range(n_train)))
random.shuffle(number_list, random = r)
shuffle_idx = torch.tensor(number_list)
train_x_f, train_y_f = train["flow_mask"][shuffle_idx], train["flow"][shuffle_idx]
train_x_o, train_y_o = train["occupancy_mask"][shuffle_idx], train["occupancy"][shuffle_idx]
delta = hyper["delta_ratio"] * f_std
aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss =\
train_epoch(model, opt, criterion, train_x_f.to(device), train_y_f,\
train_x_o.to(device), train_y_o, hyper, flow_std_squ, delta)
opt_scheduler.step()
total_phy_flow_occ_loss.append([aver_loss, aver_p_loss, aver_f_loss, aver_o_loss])
print ("train loss for this epoch: ", round(aver_loss, 6))
#evaluate
b_s_vt = hyper["b_s_vt"]
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
evaluate(model, vali_f, vali_o, vali_f_m, vali_o_m, f_o_mean_std, b_s_vt)
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
evaluate(model, test_f, test_o, test_f_m, test_o_m, f_o_mean_std, b_s_vt)
r_vali_f_mae.append(vali_f_mae)
r_test_f_mae.append(test_f_mae)
r_vali_o_mae.append(vali_o_mae)
r_test_o_mae.append(test_o_mae)
r_vali_f_rmse.append(vali_f_rmse)
r_test_f_rmse.append(test_f_rmse)
r_vali_o_rmse.append(vali_o_rmse)
r_test_o_rmse.append(test_o_rmse)
visualize_train_loss(total_phy_flow_occ_loss)
visualize_flow_loss(r_vali_f_mae, r_test_f_mae)
visualize_occ_loss(r_vali_o_mae, r_test_o_mae)
time_e = time.time()
print ("time for this epoch", time_e - time_s)
performance = {"train": total_phy_flow_occ_loss,\
"vali": [r_vali_f_mae, r_vali_f_rmse, r_vali_o_mae, r_vali_o_rmse],\
"test": [r_test_f_mae, r_test_f_rmse, r_test_o_mae, r_test_o_rmse]}
subfile = open(file_name + '/' + 'performance'+'.json','w')
json.dump(performance, subfile)
subfile.close()
#early stop
flow_std, occ_std = f_o_mean_std[1], f_o_mean_std[3]
norm_f_rmse, norm_o_rmse = vali_f_rmse/flow_std, vali_o_rmse/occ_std
norm_sum_mse = norm_f_rmse*norm_f_rmse + norm_o_rmse*norm_o_rmse
if n_mse_flow_occ > 0:
min_until_now = min([min_until_now, norm_sum_mse])
else:
min_until_now = 1000000.0
if norm_sum_mse > min_until_now:
no_decrease = no_decrease+1
else:
no_decrease = 0
if no_decrease == max_no_decrease:
print ("Early stop at the " + str(i+1) + "-th epoch")
return total_phy_flow_occ_loss, model
n_mse_flow_occ = n_mse_flow_occ + 1
print ("No_decrease: ", no_decrease)
return total_phy_flow_occ_loss, model
def tensorize(train_vali_test):
result = dict()
result["flow"] = torch.tensor(train_vali_test["flow"])
result["flow_mask"] = torch.tensor(train_vali_test["flow_mask"])
result["occupancy"] = torch.tensor(train_vali_test["occupancy"])
result["occupancy_mask"] = torch.tensor(train_vali_test["occupancy_mask"])
return result
def normalize_flow_occ(tvt, f_o_mean_std): #tvt: train, vali, test
#flow
f_mean, f_std = f_o_mean_std[0], f_o_mean_std[1]
f_mask, f = tvt["flow_mask"], tvt["flow"]
tvt["flow_mask"] = ((np.array(f_mask)-f_mean)/f_std).tolist()
tvt["flow"] = ((np.array(f)-f_mean)/f_std).tolist()
#occ
o_mean, o_std = f_o_mean_std[2], f_o_mean_std[3]
o_mask, o = tvt["occupancy_mask"], tvt["occupancy"]
tvt["occupancy_mask"] = ((np.array(o_mask)-o_mean)/o_std).tolist()
tvt["occupancy"] = ((np.array(o)-o_mean)/o_std).tolist()
return tvt
def transform_distance(d_matrix):
sigma, n_row, n_col = np.std(d_matrix), len(d_matrix), len(d_matrix[0])
sigma_square = sigma*sigma
for i in range(n_row):
for j in range(n_col):
d_i_j = d_matrix[i][j]
d_matrix[i][j] = np.exp(0.0-10000.0*d_i_j*d_i_j/sigma_square)
return d_matrix
def load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path):
mean_std = json.load(open(mean_std_path))
f_mean, f_std, o_mean, o_std =\
mean_std["f_mean"], mean_std["f_std"], mean_std["o_mean"], mean_std["o_std"]
f_o_mean_std = [f_mean, f_std, o_mean, o_std]
train = json.load(open(train_path))
vali = json.load(open(vali_path))
test = json.load(open(test_path))
adj = json.load(open(sensor_adj_path))["adj"]
n_sensor = len(train["flow"][0])
train = tensorize(normalize_flow_occ(train, f_o_mean_std))
vali = tensorize(normalize_flow_occ(vali, f_o_mean_std))
test = tensorize(normalize_flow_occ(test, f_o_mean_std))
adj = torch.tensor(transform_distance(adj), device=device).float()
df_sensor_id = json.load(open(sensor_id_path))
sensor_length = [0.0 for i in range(n_sensor)]
for sensor in df_sensor_id:
sensor_length[df_sensor_id[sensor][0]] = df_sensor_id[sensor][3]
return train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length
#6.1 load the data
time1 = time.time()
train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length =\
load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path)
time2 = time.time()
print (time2-time1)
8.184683561325073
print (len(train["flow"]))
print (len(vali["flow"]))
print (len(test["flow"]))
print (f_o_mean_std)
1546 509 510 [426.01546608926594, 254.82043085525967, 0.1815290985289925, 0.18313943695883658]
model = NMFD_GNN(n_sensor, M, hyper_model, f_o_mean_std, sensor_length, adj).to(device)
cri = nn.MSELoss()
#6.2: train the model
total_phy_flow_occ_loss, trained_model = train_process(model, cri, train, vali, test, hyper, f_o_mean_std)
# epochs 200 ----------------an epoch starts------------------- i_epoch: 0 # batch: 96 i_batch: 0.0 the loss for this batch: 1.4848627 flow loss 0.96840364 occ loss 0.5164554 time for this batch 0.5767495632171631 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.5330178 flow loss 0.29276595 occ loss 0.24024825 time for this batch 0.2957487106323242 ---------------------------------- train loss for this epoch: 0.67295
time for this epoch 35.35088920593262 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 1 # batch: 96 i_batch: 0.0 the loss for this batch: 0.504915 flow loss 0.2062798 occ loss 0.29863065 time for this batch 0.2524745464324951 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.46207827 flow loss 0.2263971 occ loss 0.23567651 time for this batch 0.2880067825317383 ---------------------------------- train loss for this epoch: 0.374251
time for this epoch 36.26380181312561 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 2 # batch: 96 i_batch: 0.0 the loss for this batch: 0.38819546 flow loss 0.18900731 occ loss 0.19918442 time for this batch 0.25884556770324707 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.3044828 flow loss 0.14840803 occ loss 0.15607126 time for this batch 0.3065369129180908 ---------------------------------- train loss for this epoch: 0.328263
time for this epoch 35.72489857673645 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 3 # batch: 96 i_batch: 0.0 the loss for this batch: 0.38382387 flow loss 0.1616818 occ loss 0.2221377 time for this batch 0.2742955684661865 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24658458 flow loss 0.12667866 occ loss 0.11990277 time for this batch 0.2919182777404785 ---------------------------------- train loss for this epoch: 0.304061
time for this epoch 36.43282461166382 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 4 # batch: 96 i_batch: 0.0 the loss for this batch: 0.30753264 flow loss 0.13512178 occ loss 0.17240673 time for this batch 0.2702476978302002 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.29918933 flow loss 0.14269549 occ loss 0.15648997 time for this batch 0.3111114501953125 ---------------------------------- train loss for this epoch: 0.287548
time for this epoch 36.76224446296692 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 5 # batch: 96 i_batch: 0.0 the loss for this batch: 0.29134503 flow loss 0.15168162 occ loss 0.13966066 time for this batch 0.27826666831970215 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.26444674 flow loss 0.12989955 occ loss 0.13454352 time for this batch 0.2848038673400879 ---------------------------------- train loss for this epoch: 0.267849
time for this epoch 36.37389659881592 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 6 # batch: 96 i_batch: 0.0 the loss for this batch: 0.24478365 flow loss 0.10612664 occ loss 0.13865367 time for this batch 0.27057385444641113 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.26467466 flow loss 0.11512673 occ loss 0.14954412 time for this batch 0.30602574348449707 ---------------------------------- train loss for this epoch: 0.254552
time for this epoch 36.506502866744995 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 7 # batch: 96 i_batch: 0.0 the loss for this batch: 0.22008352 flow loss 0.11125137 occ loss 0.10882893 time for this batch 0.26857757568359375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2270084 flow loss 0.10302466 occ loss 0.12397967 time for this batch 0.307401180267334 ---------------------------------- train loss for this epoch: 0.248707
time for this epoch 36.476739168167114 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 8 # batch: 96 i_batch: 0.0 the loss for this batch: 0.24095438 flow loss 0.111260995 occ loss 0.12969047 time for this batch 0.26579713821411133 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2455107 flow loss 0.101134025 occ loss 0.14437304 time for this batch 0.3143503665924072 ---------------------------------- train loss for this epoch: 0.239479
time for this epoch 36.11579632759094 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 9 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21575913 flow loss 0.09722692 occ loss 0.11852911 time for this batch 0.27146196365356445 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22005168 flow loss 0.10042532 occ loss 0.11962275 time for this batch 0.3151531219482422 ---------------------------------- train loss for this epoch: 0.234619
time for this epoch 37.074735164642334 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 10 # batch: 96 i_batch: 0.0 the loss for this batch: 0.27981463 flow loss 0.10427543 occ loss 0.175535 time for this batch 0.2644953727722168 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2359231 flow loss 0.10845666 occ loss 0.12746277 time for this batch 0.30802297592163086 ---------------------------------- train loss for this epoch: 0.230569
time for this epoch 36.481016874313354 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 11 # batch: 96 i_batch: 0.0 the loss for this batch: 0.25549978 flow loss 0.090290025 occ loss 0.1652063 time for this batch 0.2687368392944336 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21342996 flow loss 0.100876875 occ loss 0.11254953 time for this batch 0.2679259777069092 ---------------------------------- train loss for this epoch: 0.229597
time for this epoch 36.01028394699097 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 12 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21924475 flow loss 0.08504423 occ loss 0.13419698 time for this batch 0.2635970115661621 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18196929 flow loss 0.08549277 occ loss 0.096472986 time for this batch 0.30960702896118164 ---------------------------------- train loss for this epoch: 0.222377
time for this epoch 36.02855658531189 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 13 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1860116 flow loss 0.08458998 occ loss 0.101418346 time for this batch 0.261707067489624 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24673149 flow loss 0.09567285 occ loss 0.15105446 time for this batch 0.29612064361572266 ---------------------------------- train loss for this epoch: 0.219995
time for this epoch 37.14602303504944 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 14 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1947659 flow loss 0.08510791 occ loss 0.109654725 time for this batch 0.22641491889953613 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22287405 flow loss 0.08489458 occ loss 0.13797498 time for this batch 0.3033750057220459 ---------------------------------- train loss for this epoch: 0.217076
time for this epoch 36.83862829208374 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 15 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17047372 flow loss 0.07279774 occ loss 0.09767276 time for this batch 0.2696096897125244 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22185308 flow loss 0.08148939 occ loss 0.14036043 time for this batch 0.29004430770874023 ---------------------------------- train loss for this epoch: 0.215989
time for this epoch 36.49088501930237 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 16 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21889906 flow loss 0.0786569 occ loss 0.14023878 time for this batch 0.27796173095703125 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20558225 flow loss 0.08659989 occ loss 0.118978955 time for this batch 0.3019697666168213 ---------------------------------- train loss for this epoch: 0.213245
time for this epoch 36.54662370681763 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 17 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2195569 flow loss 0.08192747 occ loss 0.13762589 time for this batch 0.260636568069458 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18881476 flow loss 0.077533394 occ loss 0.11127805 time for this batch 0.30052924156188965 ---------------------------------- train loss for this epoch: 0.211795
time for this epoch 36.75710964202881 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 18 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1934085 flow loss 0.08154051 occ loss 0.11186494 time for this batch 0.2787659168243408 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18356383 flow loss 0.08387903 occ loss 0.09968159 time for this batch 0.30490875244140625 ---------------------------------- train loss for this epoch: 0.209775
time for this epoch 36.953593015670776 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 19 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2401194 flow loss 0.09295917 occ loss 0.14715616 time for this batch 0.28457212448120117 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23262028 flow loss 0.09302713 occ loss 0.1395889 time for this batch 0.28689122200012207 ---------------------------------- train loss for this epoch: 0.210994
time for this epoch 36.93781065940857 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 20 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18188073 flow loss 0.07853105 occ loss 0.10334596 time for this batch 0.2651689052581787 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16225863 flow loss 0.0779114 occ loss 0.084344156 time for this batch 0.26715898513793945 ---------------------------------- train loss for this epoch: 0.207553
time for this epoch 36.71192169189453 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 21 # batch: 96 i_batch: 0.0 the loss for this batch: 0.22268672 flow loss 0.07882426 occ loss 0.14385857 time for this batch 0.2740635871887207 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19573662 flow loss 0.08823739 occ loss 0.107495286 time for this batch 0.2738208770751953 ---------------------------------- train loss for this epoch: 0.205157
time for this epoch 36.22328162193298 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 22 # batch: 96 i_batch: 0.0 the loss for this batch: 0.22048828 flow loss 0.08721264 occ loss 0.1332718 time for this batch 0.26253271102905273 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22229864 flow loss 0.08217304 occ loss 0.14012223 time for this batch 0.28227829933166504 ---------------------------------- train loss for this epoch: 0.2059
time for this epoch 35.55528903007507 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 23 # batch: 96 i_batch: 0.0 the loss for this batch: 0.22821781 flow loss 0.08434923 occ loss 0.14386481 time for this batch 0.2720198631286621 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24997693 flow loss 0.09495654 occ loss 0.15501618 time for this batch 0.29602837562561035 ---------------------------------- train loss for this epoch: 0.20324
time for this epoch 37.21440505981445 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 24 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1917189 flow loss 0.07253474 occ loss 0.11918025 time for this batch 0.2591521739959717 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22302781 flow loss 0.08475726 occ loss 0.13826638 time for this batch 0.3020796775817871 ---------------------------------- train loss for this epoch: 0.20225
time for this epoch 36.53205847740173 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 25 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20427763 flow loss 0.08246567 occ loss 0.12180783 time for this batch 0.2750389575958252 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20641851 flow loss 0.081985965 occ loss 0.124428675 time for this batch 0.30574846267700195 ---------------------------------- train loss for this epoch: 0.200115
time for this epoch 36.2480411529541 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 26 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18382381 flow loss 0.07596087 occ loss 0.107859656 time for this batch 0.26570773124694824 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.25091738 flow loss 0.087745525 occ loss 0.16316763 time for this batch 0.3085489273071289 ---------------------------------- train loss for this epoch: 0.200239
time for this epoch 36.809396266937256 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 27 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18273313 flow loss 0.071750864 occ loss 0.11097878 time for this batch 0.25365757942199707 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15688139 flow loss 0.070483096 occ loss 0.08639549 time for this batch 0.30730485916137695 ---------------------------------- train loss for this epoch: 0.199173
time for this epoch 35.70003390312195 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 28 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18956283 flow loss 0.082961835 occ loss 0.10659733 time for this batch 0.2703700065612793 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21013807 flow loss 0.07582954 occ loss 0.13430525 time for this batch 0.30036473274230957 ---------------------------------- train loss for this epoch: 0.198323
time for this epoch 36.33100438117981 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 29 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21135007 flow loss 0.07876875 occ loss 0.13257745 time for this batch 0.25034642219543457 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2038164 flow loss 0.07802365 occ loss 0.12578885 time for this batch 0.29460811614990234 ---------------------------------- train loss for this epoch: 0.196068
time for this epoch 36.5717294216156 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 30 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15656552 flow loss 0.06816195 occ loss 0.088400185 time for this batch 0.26603150367736816 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23535305 flow loss 0.08681808 occ loss 0.14853051 time for this batch 0.3082709312438965 ---------------------------------- train loss for this epoch: 0.196247
time for this epoch 36.39078092575073 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 31 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21093604 flow loss 0.07945277 occ loss 0.13147928 time for this batch 0.29892396926879883 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23003227 flow loss 0.07967452 occ loss 0.15035376 time for this batch 0.3144857883453369 ---------------------------------- train loss for this epoch: 0.194247
time for this epoch 36.363056659698486 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 32 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16577658 flow loss 0.07009601 occ loss 0.095677584 time for this batch 0.2851865291595459 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20187029 flow loss 0.08237076 occ loss 0.11949633 time for this batch 0.3075888156890869 ---------------------------------- train loss for this epoch: 0.19523
time for this epoch 36.88019013404846 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 33 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16595392 flow loss 0.06682661 occ loss 0.09912397 time for this batch 0.27524614334106445 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1997519 flow loss 0.079427235 occ loss 0.12032048 time for this batch 0.3018186092376709 ---------------------------------- train loss for this epoch: 0.191345
time for this epoch 36.57381319999695 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 34 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16905616 flow loss 0.07144752 occ loss 0.09760567 time for this batch 0.2635214328765869 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21878001 flow loss 0.08692749 occ loss 0.13184842 time for this batch 0.29453229904174805 ---------------------------------- train loss for this epoch: 0.192792
time for this epoch 36.56272745132446 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 35 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1751798 flow loss 0.07813632 occ loss 0.097040124 time for this batch 0.28175997734069824 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18963453 flow loss 0.07237668 occ loss 0.11725437 time for this batch 0.2951846122741699 ---------------------------------- train loss for this epoch: 0.190079
time for this epoch 37.18488931655884 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 36 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18572682 flow loss 0.07366733 occ loss 0.112055466 time for this batch 0.2374277114868164 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21070947 flow loss 0.08351169 occ loss 0.12719388 time for this batch 0.2873697280883789 ---------------------------------- train loss for this epoch: 0.190372
time for this epoch 36.48785901069641 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 37 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17833066 flow loss 0.06895015 occ loss 0.10937741 time for this batch 0.24336719512939453 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15828165 flow loss 0.07491159 occ loss 0.08336729 time for this batch 0.27527856826782227 ---------------------------------- train loss for this epoch: 0.18904
time for this epoch 35.32898545265198 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 38 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19937101 flow loss 0.08110639 occ loss 0.11826072 time for this batch 0.2968564033508301 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18770187 flow loss 0.071989425 occ loss 0.11570901 time for this batch 0.2882812023162842 ---------------------------------- train loss for this epoch: 0.188092
time for this epoch 37.20861530303955 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 39 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2059547 flow loss 0.08343612 occ loss 0.122514345 time for this batch 0.27173566818237305 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17996256 flow loss 0.0725708 occ loss 0.10738864 time for this batch 0.2989482879638672 ---------------------------------- train loss for this epoch: 0.187626
time for this epoch 35.988420486450195 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 40 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17026931 flow loss 0.06977387 occ loss 0.10049239 time for this batch 0.29137110710144043 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21843845 flow loss 0.0869526 occ loss 0.13148217 time for this batch 0.29509997367858887 ---------------------------------- train loss for this epoch: 0.192335
time for this epoch 35.81845259666443 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 41 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17741044 flow loss 0.07096579 occ loss 0.106441356 time for this batch 0.25344324111938477 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13557583 flow loss 0.07164313 occ loss 0.063929975 time for this batch 0.32034921646118164 ---------------------------------- train loss for this epoch: 0.187096
time for this epoch 36.910390853881836 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 42 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21570982 flow loss 0.080457695 occ loss 0.13524796 time for this batch 0.2680227756500244 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1924775 flow loss 0.077112556 occ loss 0.11536118 time for this batch 0.2730860710144043 ---------------------------------- train loss for this epoch: 0.187118
time for this epoch 34.674721240997314 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 43 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17942195 flow loss 0.076459266 occ loss 0.10295859 time for this batch 0.279766321182251 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17702039 flow loss 0.07085346 occ loss 0.10616301 time for this batch 0.28073811531066895 ---------------------------------- train loss for this epoch: 0.185205
time for this epoch 35.67924904823303 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 44 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18513392 flow loss 0.06956361 occ loss 0.11556615 time for this batch 0.2591056823730469 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17918205 flow loss 0.06628051 occ loss 0.11289803 time for this batch 0.2177410125732422 ---------------------------------- train loss for this epoch: 0.184943
time for this epoch 35.05337119102478 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 45 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1823433 flow loss 0.07246921 occ loss 0.10987043 time for this batch 0.26689743995666504 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17665713 flow loss 0.07267065 occ loss 0.10398312 time for this batch 0.2892742156982422 ---------------------------------- train loss for this epoch: 0.183888
time for this epoch 34.55796837806702 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 46 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17663854 flow loss 0.07142524 occ loss 0.10520952 time for this batch 0.254258394241333 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18716614 flow loss 0.07467027 occ loss 0.11249165 time for this batch 0.28038477897644043 ---------------------------------- train loss for this epoch: 0.182633
time for this epoch 36.276222229003906 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 47 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20345166 flow loss 0.079716824 occ loss 0.123730764 time for this batch 0.2537808418273926 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2056253 flow loss 0.08136269 occ loss 0.1242588 time for this batch 0.28861331939697266 ---------------------------------- train loss for this epoch: 0.184127
time for this epoch 35.83635854721069 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 48 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20284949 flow loss 0.077887975 occ loss 0.12495737 time for this batch 0.26705169677734375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17332548 flow loss 0.0714117 occ loss 0.10191004 time for this batch 0.31632304191589355 ---------------------------------- train loss for this epoch: 0.183839
time for this epoch 36.09261655807495 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 49 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19353382 flow loss 0.071439035 occ loss 0.12209086 time for this batch 0.24895811080932617 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16096519 flow loss 0.065679185 occ loss 0.095282644 time for this batch 0.29561424255371094 ---------------------------------- train loss for this epoch: 0.181497
time for this epoch 34.79190969467163 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 50 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17891356 flow loss 0.06977864 occ loss 0.10913079 time for this batch 0.27708888053894043 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22062956 flow loss 0.07867352 occ loss 0.14195234 time for this batch 0.28504514694213867 ---------------------------------- train loss for this epoch: 0.182917
time for this epoch 36.49173927307129 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 51 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19528167 flow loss 0.07545088 occ loss 0.11982691 time for this batch 0.21551203727722168 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19392785 flow loss 0.07273917 occ loss 0.121184886 time for this batch 0.30405616760253906 ---------------------------------- train loss for this epoch: 0.181306
time for this epoch 36.255181074142456 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 52 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2058007 flow loss 0.0760668 occ loss 0.12973003 time for this batch 0.2696704864501953 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17143835 flow loss 0.07023072 occ loss 0.1012038 time for this batch 0.3026313781738281 ---------------------------------- train loss for this epoch: 0.182572
time for this epoch 37.11858582496643 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 53 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16025512 flow loss 0.061260022 occ loss 0.09899191 time for this batch 0.26891517639160156 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1907624 flow loss 0.07023854 occ loss 0.120520286 time for this batch 0.2834939956665039 ---------------------------------- train loss for this epoch: 0.1833
time for this epoch 36.923429012298584 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 54 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18750988 flow loss 0.06658815 occ loss 0.12091818 time for this batch 0.25446319580078125 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16232912 flow loss 0.06936911 occ loss 0.09295712 time for this batch 0.295015811920166 ---------------------------------- train loss for this epoch: 0.181848
time for this epoch 36.19132423400879 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 55 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19813064 flow loss 0.0750558 occ loss 0.123070404 time for this batch 0.26606011390686035 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18697028 flow loss 0.08118334 occ loss 0.10578339 time for this batch 0.295196533203125 ---------------------------------- train loss for this epoch: 0.180924
time for this epoch 36.61938738822937 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 56 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13539164 flow loss 0.059953917 occ loss 0.07543505 time for this batch 0.27460575103759766 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16657723 flow loss 0.06371121 occ loss 0.102862574 time for this batch 0.3220505714416504 ---------------------------------- train loss for this epoch: 0.180049
time for this epoch 36.337857246398926 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 57 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16193925 flow loss 0.06744045 occ loss 0.09449542 time for this batch 0.26869654655456543 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1587363 flow loss 0.0674629 occ loss 0.09126965 time for this batch 0.3044757843017578 ---------------------------------- train loss for this epoch: 0.178834
time for this epoch 36.10396647453308 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 58 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18001497 flow loss 0.071580656 occ loss 0.10843017 time for this batch 0.26924848556518555 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24322592 flow loss 0.08231777 occ loss 0.16090374 time for this batch 0.298065185546875 ---------------------------------- train loss for this epoch: 0.180088
time for this epoch 36.44292593002319 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 59 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21036474 flow loss 0.08087902 occ loss 0.12948212 time for this batch 0.25804638862609863 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1726955 flow loss 0.071148194 occ loss 0.1015436 time for this batch 0.30953478813171387 ---------------------------------- train loss for this epoch: 0.178833
time for this epoch 36.552202463150024 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 60 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18909642 flow loss 0.07482378 occ loss 0.11426877 time for this batch 0.26317644119262695 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16021167 flow loss 0.06262844 occ loss 0.09758005 time for this batch 0.27545833587646484 ---------------------------------- train loss for this epoch: 0.177935
time for this epoch 36.789323806762695 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 61 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1753531 flow loss 0.07049825 occ loss 0.10485097 time for this batch 0.2588675022125244 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23180507 flow loss 0.08119468 occ loss 0.15060562 time for this batch 0.3041698932647705 ---------------------------------- train loss for this epoch: 0.177658
time for this epoch 36.531075954437256 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 62 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16483021 flow loss 0.06810846 occ loss 0.09671803 time for this batch 0.2862701416015625 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1707902 flow loss 0.070452645 occ loss 0.100334 time for this batch 0.30980873107910156 ---------------------------------- train loss for this epoch: 0.178709
time for this epoch 37.32961463928223 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 63 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19101085 flow loss 0.07643664 occ loss 0.11457002 time for this batch 0.26540207862854004 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17375998 flow loss 0.07352018 occ loss 0.10023622 time for this batch 0.26316332817077637 ---------------------------------- train loss for this epoch: 0.177491
time for this epoch 37.00841212272644 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 64 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16290376 flow loss 0.06458205 occ loss 0.0983183 time for this batch 0.22385144233703613 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19531809 flow loss 0.076790966 occ loss 0.118523195 time for this batch 0.3075220584869385 ---------------------------------- train loss for this epoch: 0.177791
time for this epoch 36.1899778842926 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 65 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1723732 flow loss 0.06427186 occ loss 0.1080977 time for this batch 0.27466535568237305 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1588903 flow loss 0.06846731 occ loss 0.09041987 time for this batch 0.31441783905029297 ---------------------------------- train loss for this epoch: 0.177847
time for this epoch 35.817121505737305 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 66 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15411407 flow loss 0.06406804 occ loss 0.09004247 time for this batch 0.2798013687133789 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15341407 flow loss 0.06523414 occ loss 0.08817645 time for this batch 0.2758316993713379 ---------------------------------- train loss for this epoch: 0.176361
time for this epoch 36.92623257637024 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 67 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16533963 flow loss 0.06229554 occ loss 0.10304066 time for this batch 0.23973584175109863 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18025367 flow loss 0.069517314 occ loss 0.11073259 time for this batch 0.26734161376953125 ---------------------------------- train loss for this epoch: 0.175547
time for this epoch 35.93621826171875 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 68 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14675237 flow loss 0.057074748 occ loss 0.08967435 time for this batch 0.39333009719848633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20825483 flow loss 0.078486644 occ loss 0.12976412 time for this batch 0.2895548343658447 ---------------------------------- train loss for this epoch: 0.174556
time for this epoch 36.998363971710205 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 69 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1691134 flow loss 0.06688292 occ loss 0.10222689 time for this batch 0.25583958625793457 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1819689 flow loss 0.06839092 occ loss 0.11357445 time for this batch 0.3097679615020752 ---------------------------------- train loss for this epoch: 0.175689
time for this epoch 36.78766870498657 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 70 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21485184 flow loss 0.07621759 occ loss 0.13862966 time for this batch 0.29839277267456055 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18309729 flow loss 0.068571106 occ loss 0.11452207 time for this batch 0.29604268074035645 ---------------------------------- train loss for this epoch: 0.175422
time for this epoch 36.98432946205139 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 71 # batch: 96 i_batch: 0.0 the loss for this batch: 0.23107934 flow loss 0.07968146 occ loss 0.15139344 time for this batch 0.2674062252044678 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20460348 flow loss 0.07571739 occ loss 0.12888229 time for this batch 0.2894108295440674 ---------------------------------- train loss for this epoch: 0.17474
time for this epoch 37.167922496795654 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 72 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13783316 flow loss 0.060990963 occ loss 0.076838985 time for this batch 0.24402976036071777 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19373538 flow loss 0.07186142 occ loss 0.12187013 time for this batch 0.2826354503631592 ---------------------------------- train loss for this epoch: 0.175583
time for this epoch 35.80727791786194 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 73 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1263726 flow loss 0.052411254 occ loss 0.073959 time for this batch 0.23323559761047363 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1419653 flow loss 0.060238235 occ loss 0.08172379 time for this batch 0.2734816074371338 ---------------------------------- train loss for this epoch: 0.173186
time for this epoch 35.973103523254395 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 74 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20644625 flow loss 0.072972134 occ loss 0.1334702 time for this batch 0.2493000030517578 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1816346 flow loss 0.06511269 occ loss 0.116518065 time for this batch 0.2941913604736328 ---------------------------------- train loss for this epoch: 0.172934
time for this epoch 37.26864814758301 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 75 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14022571 flow loss 0.06110737 occ loss 0.07911513 time for this batch 0.22477436065673828 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17298329 flow loss 0.06666909 occ loss 0.106310464 time for this batch 0.3136296272277832 ---------------------------------- train loss for this epoch: 0.174291
time for this epoch 36.403735637664795 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 76 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13878988 flow loss 0.057158813 occ loss 0.08162786 time for this batch 0.25097084045410156 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18907057 flow loss 0.07163599 occ loss 0.117430665 time for this batch 0.3024766445159912 ---------------------------------- train loss for this epoch: 0.172399
time for this epoch 36.01527237892151 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 77 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20286588 flow loss 0.074485816 occ loss 0.12837599 time for this batch 0.2635650634765625 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18744698 flow loss 0.06945291 occ loss 0.11798964 time for this batch 0.2990877628326416 ---------------------------------- train loss for this epoch: 0.173956
time for this epoch 36.90428328514099 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 78 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17089725 flow loss 0.064613916 occ loss 0.106279776 time for this batch 0.2455596923828125 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14160351 flow loss 0.058686413 occ loss 0.08291394 time for this batch 0.3081514835357666 ---------------------------------- train loss for this epoch: 0.172542
time for this epoch 35.707332611083984 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 79 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21988234 flow loss 0.075423725 occ loss 0.14445381 time for this batch 0.266132116317749 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19545025 flow loss 0.07247921 occ loss 0.12296689 time for this batch 0.2970151901245117 ---------------------------------- train loss for this epoch: 0.172347
time for this epoch 35.4137179851532 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 80 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20831278 flow loss 0.072078645 occ loss 0.13623023 time for this batch 0.2477560043334961 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19094275 flow loss 0.073169224 occ loss 0.117769696 time for this batch 0.29091453552246094 ---------------------------------- train loss for this epoch: 0.171351
time for this epoch 35.82119607925415 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 81 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16882126 flow loss 0.06315825 occ loss 0.105659105 time for this batch 0.26991724967956543 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19282202 flow loss 0.072176196 occ loss 0.1206419 time for this batch 0.29759764671325684 ---------------------------------- train loss for this epoch: 0.171169
time for this epoch 35.67663788795471 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 82 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16017881 flow loss 0.06233093 occ loss 0.09784419 time for this batch 0.2773168087005615 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17233604 flow loss 0.06445405 occ loss 0.10787841 time for this batch 0.29906630516052246 ---------------------------------- train loss for this epoch: 0.172369
time for this epoch 36.35960268974304 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 83 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18449962 flow loss 0.069640696 occ loss 0.114855066 time for this batch 0.27439379692077637 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17863105 flow loss 0.06338445 occ loss 0.11524329 time for this batch 0.3052985668182373 ---------------------------------- train loss for this epoch: 0.170522
time for this epoch 35.961769104003906 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 84 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16241603 flow loss 0.06163134 occ loss 0.100781 time for this batch 0.24818849563598633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15231797 flow loss 0.06019659 occ loss 0.09211821 time for this batch 0.27852916717529297 ---------------------------------- train loss for this epoch: 0.169224
time for this epoch 35.969027280807495 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 85 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14543402 flow loss 0.0646271 occ loss 0.0808033 time for this batch 0.2744300365447998 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1657066 flow loss 0.06162411 occ loss 0.10407907 time for this batch 0.24903464317321777 ---------------------------------- train loss for this epoch: 0.169875
time for this epoch 35.53133964538574 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 86 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15730384 flow loss 0.058478154 occ loss 0.098822385 time for this batch 0.2624833583831787 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14278927 flow loss 0.0584066 occ loss 0.08437944 time for this batch 0.29090261459350586 ---------------------------------- train loss for this epoch: 0.169951
time for this epoch 36.399160861968994 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 87 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16516238 flow loss 0.06617231 occ loss 0.098986454 time for this batch 0.26729822158813477 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17038675 flow loss 0.0669805 occ loss 0.10340261 time for this batch 0.2955605983734131 ---------------------------------- train loss for this epoch: 0.169764
time for this epoch 36.39837098121643 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 88 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16472793 flow loss 0.06453218 occ loss 0.10019229 time for this batch 0.24997782707214355 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19479768 flow loss 0.07145292 occ loss 0.12334092 time for this batch 0.2692372798919678 ---------------------------------- train loss for this epoch: 0.168968
time for this epoch 35.8499972820282 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 89 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1384904 flow loss 0.06287128 occ loss 0.075615786 time for this batch 0.2775707244873047 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1836156 flow loss 0.0682636 occ loss 0.11534828 time for this batch 0.2675633430480957 ---------------------------------- train loss for this epoch: 0.168052
time for this epoch 36.95125341415405 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 90 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18496582 flow loss 0.065818235 occ loss 0.11914395 time for this batch 0.24342751502990723 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17197001 flow loss 0.0641842 occ loss 0.10778219 time for this batch 0.28157806396484375 ---------------------------------- train loss for this epoch: 0.168625
time for this epoch 36.20490312576294 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 91 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16162637 flow loss 0.06214443 occ loss 0.09947826 time for this batch 0.27723097801208496 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1792115 flow loss 0.06685048 occ loss 0.11235764 time for this batch 0.31040096282958984 ---------------------------------- train loss for this epoch: 0.168336
time for this epoch 36.55529236793518 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 92 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12729232 flow loss 0.055840142 occ loss 0.07144938 time for this batch 0.26594018936157227 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18577763 flow loss 0.06695891 occ loss 0.118814744 time for this batch 0.310391902923584 ---------------------------------- train loss for this epoch: 0.167042
time for this epoch 36.72194170951843 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 93 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1689393 flow loss 0.07017335 occ loss 0.09876239 time for this batch 0.272475004196167 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14272101 flow loss 0.06409043 occ loss 0.07862693 time for this batch 0.30746960639953613 ---------------------------------- train loss for this epoch: 0.167704
time for this epoch 36.28701853752136 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 94 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17403838 flow loss 0.06892745 occ loss 0.10510692 time for this batch 0.27414655685424805 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15586485 flow loss 0.06114303 occ loss 0.09471817 time for this batch 0.3116471767425537 ---------------------------------- train loss for this epoch: 0.16856
time for this epoch 35.48454737663269 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 95 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13571046 flow loss 0.061258968 occ loss 0.074448146 time for this batch 0.2691929340362549 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16419896 flow loss 0.060844686 occ loss 0.103350736 time for this batch 0.27648019790649414 ---------------------------------- train loss for this epoch: 0.166094
time for this epoch 36.216663122177124 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 96 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17795599 flow loss 0.064249836 occ loss 0.11370232 time for this batch 0.27649688720703125 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15249228 flow loss 0.06364626 occ loss 0.08884232 time for this batch 0.19113397598266602 ---------------------------------- train loss for this epoch: 0.167743
time for this epoch 36.36919689178467 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 97 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14917515 flow loss 0.06578784 occ loss 0.08338444 time for this batch 0.2839319705963135 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13444562 flow loss 0.05417226 occ loss 0.08027055 time for this batch 0.2982370853424072 ---------------------------------- train loss for this epoch: 0.16734
time for this epoch 36.417330741882324 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 98 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21979143 flow loss 0.06818233 occ loss 0.15160508 time for this batch 0.2704615592956543 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1771244 flow loss 0.06841209 occ loss 0.10870842 time for this batch 0.32030415534973145 ---------------------------------- train loss for this epoch: 0.167038
time for this epoch 37.29658889770508 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 99 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1925268 flow loss 0.07838245 occ loss 0.114140585 time for this batch 0.2622978687286377 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18882476 flow loss 0.06906082 occ loss 0.119760185 time for this batch 0.29944682121276855 ---------------------------------- train loss for this epoch: 0.166624
time for this epoch 36.36146926879883 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 100 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1792474 flow loss 0.06926883 occ loss 0.109975085 time for this batch 0.27871274948120117 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14372413 flow loss 0.06029646 occ loss 0.083424725 time for this batch 0.24602127075195312 ---------------------------------- train loss for this epoch: 0.167185
time for this epoch 35.5762300491333 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 101 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13339686 flow loss 0.056155924 occ loss 0.07723803 time for this batch 0.2646925449371338 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18425202 flow loss 0.06316422 occ loss 0.12108395 time for this batch 0.26790809631347656 ---------------------------------- train loss for this epoch: 0.165898
time for this epoch 36.36387753486633 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 102 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19650291 flow loss 0.068439886 occ loss 0.12805936 time for this batch 0.2657010555267334 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14620928 flow loss 0.05736331 occ loss 0.0888426 time for this batch 0.3060128688812256 ---------------------------------- train loss for this epoch: 0.164963
time for this epoch 36.56157612800598 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 103 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14426914 flow loss 0.05403943 occ loss 0.09022621 time for this batch 0.25715208053588867 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14763078 flow loss 0.065690376 occ loss 0.08193676 time for this batch 0.293501615524292 ---------------------------------- train loss for this epoch: 0.165213
time for this epoch 36.5147705078125 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 104 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18624297 flow loss 0.06886202 occ loss 0.11737693 time for this batch 0.279909610748291 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15574357 flow loss 0.05956923 occ loss 0.09617124 time for this batch 0.28909850120544434 ---------------------------------- train loss for this epoch: 0.165359
time for this epoch 36.672971963882446 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 105 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16480617 flow loss 0.06395461 occ loss 0.1008476 time for this batch 0.2650613784790039 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18722355 flow loss 0.06994075 occ loss 0.11727901 time for this batch 0.3086271286010742 ---------------------------------- train loss for this epoch: 0.167727
time for this epoch 36.103089332580566 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 106 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17915331 flow loss 0.06119725 occ loss 0.117951944 time for this batch 0.2696554660797119 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14060386 flow loss 0.057017297 occ loss 0.08358305 time for this batch 0.3029000759124756 ---------------------------------- train loss for this epoch: 0.163972
time for this epoch 36.27958703041077 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 107 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1450024 flow loss 0.05720012 occ loss 0.0877989 time for this batch 0.24257516860961914 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18908204 flow loss 0.0646449 occ loss 0.124432966 time for this batch 0.29509830474853516 ---------------------------------- train loss for this epoch: 0.163483
time for this epoch 36.98398733139038 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 108 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14166337 flow loss 0.059420567 occ loss 0.08223919 time for this batch 0.2397596836090088 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14898847 flow loss 0.06551082 occ loss 0.08347442 time for this batch 0.29529261589050293 ---------------------------------- train loss for this epoch: 0.163985
time for this epoch 35.97303080558777 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 109 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18040887 flow loss 0.07181726 occ loss 0.10858748 time for this batch 0.275068998336792 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16489393 flow loss 0.06004269 occ loss 0.10484768 time for this batch 0.2732245922088623 ---------------------------------- train loss for this epoch: 0.166312
time for this epoch 35.55239748954773 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 110 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17920618 flow loss 0.062450223 occ loss 0.1167522 time for this batch 0.29524993896484375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1409356 flow loss 0.058668554 occ loss 0.082263716 time for this batch 0.31198740005493164 ---------------------------------- train loss for this epoch: 0.162209
time for this epoch 36.33540749549866 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 111 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16470754 flow loss 0.06564003 occ loss 0.09906371 time for this batch 0.30994153022766113 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19903867 flow loss 0.0701022 occ loss 0.12893248 time for this batch 0.2807607650756836 ---------------------------------- train loss for this epoch: 0.164793
time for this epoch 35.534032106399536 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 112 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17721799 flow loss 0.06598762 occ loss 0.111226484 time for this batch 0.27225279808044434 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15032166 flow loss 0.06411424 occ loss 0.0862041 time for this batch 0.3135640621185303 ---------------------------------- train loss for this epoch: 0.164446
time for this epoch 36.557472229003906 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 113 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18643053 flow loss 0.073722966 occ loss 0.112703495 time for this batch 0.28002142906188965 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14574267 flow loss 0.06084684 occ loss 0.084892206 time for this batch 0.2647273540496826 ---------------------------------- train loss for this epoch: 0.164524
time for this epoch 35.44134783744812 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 114 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15067878 flow loss 0.062534206 occ loss 0.08814162 time for this batch 0.22495460510253906 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15089117 flow loss 0.060993984 occ loss 0.0898941 time for this batch 0.2825770378112793 ---------------------------------- train loss for this epoch: 0.163745
time for this epoch 35.87050747871399 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 115 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15983786 flow loss 0.060121026 occ loss 0.09971274 time for this batch 0.2091066837310791 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17609261 flow loss 0.06151663 occ loss 0.11457251 time for this batch 0.2752385139465332 ---------------------------------- train loss for this epoch: 0.162677
time for this epoch 36.384774684906006 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 116 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1599124 flow loss 0.06316183 occ loss 0.0967466 time for this batch 0.25289082527160645 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12841833 flow loss 0.055345513 occ loss 0.07306963 time for this batch 0.30292677879333496 ---------------------------------- train loss for this epoch: 0.162527
time for this epoch 34.71121263504028 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 117 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19199927 flow loss 0.06499395 occ loss 0.12700103 time for this batch 0.26561594009399414 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18802883 flow loss 0.066949144 occ loss 0.1210755 time for this batch 0.3181309700012207 ---------------------------------- train loss for this epoch: 0.162946
time for this epoch 36.49592852592468 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 118 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15289262 flow loss 0.0609229 occ loss 0.091966294 time for this batch 0.2164320945739746 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16829242 flow loss 0.058662936 occ loss 0.10962578 time for this batch 0.29235148429870605 ---------------------------------- train loss for this epoch: 0.162736
time for this epoch 36.584577798843384 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 119 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21054873 flow loss 0.068971455 occ loss 0.14157327 time for this batch 0.2836272716522217 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14755294 flow loss 0.055179562 occ loss 0.09236987 time for this batch 0.31075525283813477 ---------------------------------- train loss for this epoch: 0.163116
time for this epoch 36.272130250930786 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 120 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18151855 flow loss 0.06581288 occ loss 0.11570166 time for this batch 0.2699167728424072 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1441034 flow loss 0.05207712 occ loss 0.09202325 time for this batch 0.3059103488922119 ---------------------------------- train loss for this epoch: 0.161799
time for this epoch 36.46512460708618 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 121 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15202309 flow loss 0.056452837 occ loss 0.0955667 time for this batch 0.2783970832824707 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17544095 flow loss 0.06358225 occ loss 0.11185445 time for this batch 0.29420018196105957 ---------------------------------- train loss for this epoch: 0.162114
time for this epoch 36.26199746131897 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 122 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17885666 flow loss 0.064737014 occ loss 0.11411582 time for this batch 0.23047900199890137 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19230288 flow loss 0.07005014 occ loss 0.12224901 time for this batch 0.29169297218322754 ---------------------------------- train loss for this epoch: 0.162112
time for this epoch 36.30904674530029 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 123 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17073213 flow loss 0.061045486 occ loss 0.10968286 time for this batch 0.2779092788696289 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15849109 flow loss 0.06268661 occ loss 0.09580027 time for this batch 0.283616304397583 ---------------------------------- train loss for this epoch: 0.163628
time for this epoch 35.89368510246277 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 124 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13628562 flow loss 0.055544436 occ loss 0.080737926 time for this batch 0.27358531951904297 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20460193 flow loss 0.07796458 occ loss 0.12663339 time for this batch 0.3051295280456543 ---------------------------------- train loss for this epoch: 0.161929
time for this epoch 36.47081518173218 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 125 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19193465 flow loss 0.063060544 occ loss 0.12887041 time for this batch 0.2526824474334717 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16662036 flow loss 0.061257236 occ loss 0.105359666 time for this batch 0.2859337329864502 ---------------------------------- train loss for this epoch: 0.161446
time for this epoch 34.478336811065674 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 126 # batch: 96 i_batch: 0.0 the loss for this batch: 0.123939276 flow loss 0.054180507 occ loss 0.06975557 time for this batch 0.27155447006225586 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14539194 flow loss 0.055904627 occ loss 0.08948406 time for this batch 0.2542872428894043 ---------------------------------- train loss for this epoch: 0.160568
time for this epoch 34.25938701629639 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 127 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16881533 flow loss 0.06177378 occ loss 0.107037924 time for this batch 0.2815520763397217 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1581173 flow loss 0.05718545 occ loss 0.10092878 time for this batch 0.2951207160949707 ---------------------------------- train loss for this epoch: 0.160911
time for this epoch 35.72840070724487 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 128 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15884277 flow loss 0.059885863 occ loss 0.09895339 time for this batch 0.27852773666381836 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16874881 flow loss 0.05865233 occ loss 0.11009253 time for this batch 0.30559730529785156 ---------------------------------- train loss for this epoch: 0.161302
time for this epoch 36.105477809906006 No_decrease: 14 ----------------an epoch starts------------------- i_epoch: 129 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1437167 flow loss 0.058015224 occ loss 0.08569791 time for this batch 0.42058491706848145 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16073495 flow loss 0.061587088 occ loss 0.0991444 time for this batch 0.28392839431762695 ---------------------------------- train loss for this epoch: 0.161577
time for this epoch 37.649142265319824 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 130 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16021949 flow loss 0.057391483 occ loss 0.10282437 time for this batch 0.279510498046875 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19075653 flow loss 0.06747203 occ loss 0.12328041 time for this batch 0.31781458854675293 ---------------------------------- train loss for this epoch: 0.161373
time for this epoch 36.5324330329895 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 131 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17288816 flow loss 0.06324413 occ loss 0.10964043 time for this batch 0.27089977264404297 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.118350185 flow loss 0.04942366 occ loss 0.06892384 time for this batch 0.31757402420043945 ---------------------------------- train loss for this epoch: 0.160112
time for this epoch 36.23045492172241 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 132 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1839291 flow loss 0.06783835 occ loss 0.116086505 time for this batch 0.26749324798583984 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1985095 flow loss 0.07016828 occ loss 0.12833713 time for this batch 0.31740641593933105 ---------------------------------- train loss for this epoch: 0.159707
time for this epoch 35.9177360534668 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 133 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14249225 flow loss 0.05624874 occ loss 0.08624006 time for this batch 0.25034546852111816 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13436459 flow loss 0.056321207 occ loss 0.0780398 time for this batch 0.30282115936279297 ---------------------------------- train loss for this epoch: 0.161514
time for this epoch 36.86523675918579 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 134 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2004257 flow loss 0.06860453 occ loss 0.13181691 time for this batch 0.2717757225036621 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15602645 flow loss 0.062557235 occ loss 0.09346588 time for this batch 0.2917780876159668 ---------------------------------- train loss for this epoch: 0.160199
time for this epoch 36.49582576751709 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 135 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14085734 flow loss 0.060759235 occ loss 0.0800945 time for this batch 0.2746298313140869 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17397623 flow loss 0.06131994 occ loss 0.11265256 time for this batch 0.30915260314941406 ---------------------------------- train loss for this epoch: 0.159597
time for this epoch 35.78676915168762 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 136 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15552105 flow loss 0.061358456 occ loss 0.09415852 time for this batch 0.26572299003601074 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18917036 flow loss 0.0652914 occ loss 0.12387487 time for this batch 0.30899477005004883 ---------------------------------- train loss for this epoch: 0.159136
time for this epoch 36.68729376792908 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 137 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17826378 flow loss 0.06304875 occ loss 0.115210935 time for this batch 0.27826428413391113 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15807465 flow loss 0.060441405 occ loss 0.097629264 time for this batch 0.28302931785583496 ---------------------------------- train loss for this epoch: 0.161887
time for this epoch 34.945910930633545 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 138 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12794045 flow loss 0.056345534 occ loss 0.07159142 time for this batch 0.2645735740661621 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16976996 flow loss 0.06251365 occ loss 0.107252404 time for this batch 0.28904271125793457 ---------------------------------- train loss for this epoch: 0.159296
time for this epoch 36.909430742263794 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 139 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1417117 flow loss 0.058905184 occ loss 0.082803264 time for this batch 0.2606220245361328 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.175949 flow loss 0.061806288 occ loss 0.1141386 time for this batch 0.30467891693115234 ---------------------------------- train loss for this epoch: 0.159963
time for this epoch 34.58863615989685 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 140 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14252816 flow loss 0.06113178 occ loss 0.08139283 time for this batch 0.2747182846069336 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12418927 flow loss 0.052286383 occ loss 0.0719 time for this batch 0.27669858932495117 ---------------------------------- train loss for this epoch: 0.159624
time for this epoch 35.7971715927124 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 141 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15764715 flow loss 0.05971233 occ loss 0.09793154 time for this batch 0.2818324565887451 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16641568 flow loss 0.06204312 occ loss 0.104368776 time for this batch 0.3014504909515381 ---------------------------------- train loss for this epoch: 0.159577
time for this epoch 34.8903706073761 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 142 # batch: 96 i_batch: 0.0 the loss for this batch: 0.146142 flow loss 0.055916335 occ loss 0.09022186 time for this batch 0.2674839496612549 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18731645 flow loss 0.06892279 occ loss 0.11839003 time for this batch 0.3254125118255615 ---------------------------------- train loss for this epoch: 0.159566
time for this epoch 35.38903331756592 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 143 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14505488 flow loss 0.062865175 occ loss 0.08218622 time for this batch 0.2435321807861328 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16844288 flow loss 0.06348314 occ loss 0.10495596 time for this batch 0.29888248443603516 ---------------------------------- train loss for this epoch: 0.158611
time for this epoch 35.91069006919861 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 144 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21453293 flow loss 0.0684124 occ loss 0.14611627 time for this batch 0.27896666526794434 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1517582 flow loss 0.05938289 occ loss 0.09237152 time for this batch 0.2793691158294678 ---------------------------------- train loss for this epoch: 0.159632
time for this epoch 35.34902381896973 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 145 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18041235 flow loss 0.06303583 occ loss 0.11737269 time for this batch 0.2442793846130371 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1606298 flow loss 0.060738277 occ loss 0.09988796 time for this batch 0.26990580558776855 ---------------------------------- train loss for this epoch: 0.158592
time for this epoch 35.56690835952759 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 146 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12781459 flow loss 0.05256058 occ loss 0.07525085 time for this batch 0.23339438438415527 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14040744 flow loss 0.054408 occ loss 0.085996084 time for this batch 0.30510544776916504 ---------------------------------- train loss for this epoch: 0.158164
time for this epoch 35.131457805633545 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 147 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14862797 flow loss 0.055819783 occ loss 0.092805006 time for this batch 0.2700660228729248 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.192496 flow loss 0.06613547 occ loss 0.12635626 time for this batch 0.31427884101867676 ---------------------------------- train loss for this epoch: 0.158034
time for this epoch 36.332279682159424 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 148 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19374706 flow loss 0.066885725 occ loss 0.12685724 time for this batch 0.24476408958435059 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15797669 flow loss 0.061485022 occ loss 0.09648786 time for this batch 0.27745485305786133 ---------------------------------- train loss for this epoch: 0.158299
time for this epoch 35.10307860374451 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 149 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14578533 flow loss 0.054816984 occ loss 0.09096462 time for this batch 0.2791123390197754 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.11963681 flow loss 0.050678685 occ loss 0.06895516 time for this batch 0.2762587070465088 ---------------------------------- train loss for this epoch: 0.158567
time for this epoch 35.49077749252319 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 150 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12810622 flow loss 0.05794647 occ loss 0.070156336 time for this batch 0.2563741207122803 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16514343 flow loss 0.05682362 occ loss 0.10831585 time for this batch 0.28494906425476074 ---------------------------------- train loss for this epoch: 0.151692
time for this epoch 36.827033281326294 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 151 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13217722 flow loss 0.055311784 occ loss 0.076861866 time for this batch 0.2642040252685547 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15887071 flow loss 0.05993556 occ loss 0.09893129 time for this batch 0.27207446098327637 ---------------------------------- train loss for this epoch: 0.149474
time for this epoch 34.97927927970886 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 152 # batch: 96 i_batch: 0.0 the loss for this batch: 0.123123184 flow loss 0.0505635 occ loss 0.072556406 time for this batch 0.24546146392822266 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16923644 flow loss 0.057722855 occ loss 0.11150954 time for this batch 0.28682899475097656 ---------------------------------- train loss for this epoch: 0.149566
time for this epoch 35.53329157829285 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 153 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15054187 flow loss 0.05438765 occ loss 0.09615054 time for this batch 0.2723958492279053 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15965675 flow loss 0.058533467 occ loss 0.101119444 time for this batch 0.2933378219604492 ---------------------------------- train loss for this epoch: 0.149114
time for this epoch 35.965455055236816 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 154 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13708119 flow loss 0.056724064 occ loss 0.08035376 time for this batch 0.27086973190307617 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15803601 flow loss 0.055397097 occ loss 0.102635324 time for this batch 0.31653475761413574 ---------------------------------- train loss for this epoch: 0.149293
time for this epoch 36.268746852874756 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 155 # batch: 96 i_batch: 0.0 the loss for this batch: 0.09862001 flow loss 0.04666235 occ loss 0.05195507 time for this batch 0.3024623394012451 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16026913 flow loss 0.057780832 occ loss 0.10248493 time for this batch 0.3174855709075928 ---------------------------------- train loss for this epoch: 0.148643
time for this epoch 36.87047815322876 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 156 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15572418 flow loss 0.057452306 occ loss 0.098268464 time for this batch 0.27608227729797363 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1635799 flow loss 0.056331094 occ loss 0.107244976 time for this batch 0.27466535568237305 ---------------------------------- train loss for this epoch: 0.148443
time for this epoch 35.80942678451538 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 157 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14581478 flow loss 0.054201234 occ loss 0.09160999 time for this batch 0.27735304832458496 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19828668 flow loss 0.065415576 occ loss 0.13286671 time for this batch 0.2568080425262451 ---------------------------------- train loss for this epoch: 0.148535
time for this epoch 35.459089517593384 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 158 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14719172 flow loss 0.054666243 occ loss 0.092522085 time for this batch 0.2786402702331543 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13834289 flow loss 0.055291 occ loss 0.08304844 time for this batch 0.30443739891052246 ---------------------------------- train loss for this epoch: 0.148349
time for this epoch 36.783923387527466 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 159 # batch: 96 i_batch: 0.0 the loss for this batch: 0.160526 flow loss 0.05679873 occ loss 0.1037235 time for this batch 0.27070021629333496 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13834481 flow loss 0.054114923 occ loss 0.08422666 time for this batch 0.31406617164611816 ---------------------------------- train loss for this epoch: 0.148126
time for this epoch 35.626418590545654 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 160 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13770647 flow loss 0.055006906 occ loss 0.0826959 time for this batch 0.28197598457336426 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17843185 flow loss 0.059375595 occ loss 0.11905219 time for this batch 0.27596330642700195 ---------------------------------- train loss for this epoch: 0.14823
time for this epoch 36.949193477630615 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 161 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12057901 flow loss 0.049236186 occ loss 0.07133965 time for this batch 0.2742323875427246 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14266214 flow loss 0.0536653 occ loss 0.08899361 time for this batch 0.25426793098449707 ---------------------------------- train loss for this epoch: 0.148686
time for this epoch 35.92555499076843 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 162 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13300613 flow loss 0.05293093 occ loss 0.08007183 time for this batch 0.26070356369018555 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13962746 flow loss 0.053611625 occ loss 0.08601251 time for this batch 0.3038301467895508 ---------------------------------- train loss for this epoch: 0.148328
time for this epoch 36.852476358413696 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 163 # batch: 96 i_batch: 0.0 the loss for this batch: 0.121926196 flow loss 0.05054193 occ loss 0.07138091 time for this batch 0.2625150680541992 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15320995 flow loss 0.055076167 occ loss 0.09812964 time for this batch 0.3072185516357422 ---------------------------------- train loss for this epoch: 0.148273
time for this epoch 36.493934869766235 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 164 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1599451 flow loss 0.05622095 occ loss 0.1037204 time for this batch 0.32819652557373047 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15655085 flow loss 0.05764683 occ loss 0.09889996 time for this batch 0.29054689407348633 ---------------------------------- train loss for this epoch: 0.148249
time for this epoch 37.230945348739624 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 165 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12497856 flow loss 0.0517163 occ loss 0.073259 time for this batch 0.2600288391113281 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15341625 flow loss 0.055854034 occ loss 0.09755866 time for this batch 0.29476237297058105 ---------------------------------- train loss for this epoch: 0.148194
time for this epoch 36.003459453582764 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 166 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16418839 flow loss 0.05628429 occ loss 0.1079 time for this batch 0.23946309089660645 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19118255 flow loss 0.0626505 occ loss 0.1285279 time for this batch 0.3184168338775635 ---------------------------------- train loss for this epoch: 0.148222
time for this epoch 36.73204684257507 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 167 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16553316 flow loss 0.054340076 occ loss 0.111189485 time for this batch 0.25547075271606445 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1559886 flow loss 0.053324893 occ loss 0.10266001 time for this batch 0.2749302387237549 ---------------------------------- train loss for this epoch: 0.147651
time for this epoch 36.6414155960083 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 168 # batch: 96 i_batch: 0.0 the loss for this batch: 0.09743351 flow loss 0.043516714 occ loss 0.05391389 time for this batch 0.2649660110473633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13244443 flow loss 0.05187124 occ loss 0.080569394 time for this batch 0.28249597549438477 ---------------------------------- train loss for this epoch: 0.147857
time for this epoch 35.86417818069458 No_decrease: 14 ----------------an epoch starts------------------- i_epoch: 169 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12791409 flow loss 0.052431818 occ loss 0.07547886 time for this batch 0.2996053695678711 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16294289 flow loss 0.059939656 occ loss 0.10299928 time for this batch 0.25591039657592773 ---------------------------------- train loss for this epoch: 0.147864
time for this epoch 35.234233379364014 No_decrease: 15 ----------------an epoch starts------------------- i_epoch: 170 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14369164 flow loss 0.052479655 occ loss 0.09120847 time for this batch 0.26120805740356445 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13683699 flow loss 0.05376484 occ loss 0.083068736 time for this batch 0.3128654956817627 ---------------------------------- train loss for this epoch: 0.148016
time for this epoch 37.6161732673645 No_decrease: 16 ----------------an epoch starts------------------- i_epoch: 171 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1421266 flow loss 0.05348181 occ loss 0.088641025 time for this batch 0.2354123592376709 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14119884 flow loss 0.05177046 occ loss 0.0894247 time for this batch 0.309612512588501 ---------------------------------- train loss for this epoch: 0.147895
time for this epoch 36.667091846466064 No_decrease: 17 ----------------an epoch starts------------------- i_epoch: 172 # batch: 96 i_batch: 0.0 the loss for this batch: 0.118515715 flow loss 0.050481316 occ loss 0.06803116 time for this batch 0.23918485641479492 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15910368 flow loss 0.056139506 occ loss 0.102960154 time for this batch 0.3157031536102295 ---------------------------------- train loss for this epoch: 0.147847
time for this epoch 36.326873540878296 No_decrease: 18 ----------------an epoch starts------------------- i_epoch: 173 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15701742 flow loss 0.055181738 occ loss 0.10183202 time for this batch 0.2698826789855957 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16998495 flow loss 0.056493104 occ loss 0.113487974 time for this batch 0.3132328987121582 ---------------------------------- train loss for this epoch: 0.147683
time for this epoch 36.75192904472351 No_decrease: 19 ----------------an epoch starts------------------- i_epoch: 174 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16548747 flow loss 0.05941746 occ loss 0.10606601 time for this batch 0.25651073455810547 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12980081 flow loss 0.04741963 occ loss 0.08237811 time for this batch 0.3014044761657715 ---------------------------------- train loss for this epoch: 0.147649
time for this epoch 37.2434356212616 No_decrease: 20 ----------------an epoch starts------------------- i_epoch: 175 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13429709 flow loss 0.04646454 occ loss 0.08782978 time for this batch 0.26686596870422363 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13009898 flow loss 0.050194006 occ loss 0.07990201 time for this batch 0.30699968338012695 ---------------------------------- train loss for this epoch: 0.147635
time for this epoch 36.66208600997925 No_decrease: 21 ----------------an epoch starts------------------- i_epoch: 176 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17292935 flow loss 0.05908982 occ loss 0.11383555 time for this batch 0.2614457607269287 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14736788 flow loss 0.05609841 occ loss 0.091265574 time for this batch 0.24894332885742188 ---------------------------------- train loss for this epoch: 0.147607
time for this epoch 36.20180106163025 No_decrease: 22 ----------------an epoch starts------------------- i_epoch: 177 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11218548 flow loss 0.04676508 occ loss 0.06541755 time for this batch 0.2622804641723633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14724809 flow loss 0.05475111 occ loss 0.092492886 time for this batch 0.31412839889526367 ---------------------------------- train loss for this epoch: 0.147755
time for this epoch 36.29287052154541 No_decrease: 23 ----------------an epoch starts------------------- i_epoch: 178 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16172826 flow loss 0.058760982 occ loss 0.10296306 time for this batch 0.2830810546875 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1763635 flow loss 0.060359105 occ loss 0.116000235 time for this batch 0.28503966331481934 ---------------------------------- train loss for this epoch: 0.148071
time for this epoch 37.66193222999573 No_decrease: 24 ----------------an epoch starts------------------- i_epoch: 179 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13073285 flow loss 0.051524747 occ loss 0.079204805 time for this batch 0.24927401542663574 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.11243472 flow loss 0.044522107 occ loss 0.067909665 time for this batch 0.30908966064453125 ---------------------------------- train loss for this epoch: 0.147643
time for this epoch 38.01120924949646 No_decrease: 25 ----------------an epoch starts------------------- i_epoch: 180 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1802071 flow loss 0.06100902 occ loss 0.1191938 time for this batch 0.2650148868560791 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13046998 flow loss 0.04770943 occ loss 0.082757466 time for this batch 0.309734582901001 ---------------------------------- train loss for this epoch: 0.147751
time for this epoch 37.02081608772278 No_decrease: 26 ----------------an epoch starts------------------- i_epoch: 181 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14609714 flow loss 0.05566163 occ loss 0.09043176 time for this batch 0.25281548500061035 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13852595 flow loss 0.051228877 occ loss 0.087293245 time for this batch 0.30706357955932617 ---------------------------------- train loss for this epoch: 0.147479
time for this epoch 36.8151068687439 No_decrease: 27 ----------------an epoch starts------------------- i_epoch: 182 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13818757 flow loss 0.05411706 occ loss 0.08406724 time for this batch 0.2726554870605469 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17863788 flow loss 0.060442675 occ loss 0.11819139 time for this batch 0.28109145164489746 ---------------------------------- train loss for this epoch: 0.147548
time for this epoch 35.303800106048584 No_decrease: 28 ----------------an epoch starts------------------- i_epoch: 183 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1214304 flow loss 0.04717075 occ loss 0.074256726 time for this batch 0.2654991149902344 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15354057 flow loss 0.056814034 occ loss 0.09672306 time for this batch 0.27810001373291016 ---------------------------------- train loss for this epoch: 0.147498
time for this epoch 35.49075365066528 No_decrease: 29 ----------------an epoch starts------------------- i_epoch: 184 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1360121 flow loss 0.055004288 occ loss 0.08100446 time for this batch 0.26726794242858887 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18823014 flow loss 0.05962501 occ loss 0.12860096 time for this batch 0.29994893074035645 ---------------------------------- train loss for this epoch: 0.147601
time for this epoch 36.23290753364563 Early stop at the 185-th epoch
def apply_to_vali_test(model, vt, f_o_mean_std):
f = vt["flow"]
f_m = vt["flow_mask"].to(device)
o = vt["occupancy"]
o_m = vt["occupancy_mask"].to(device)
f_mae, f_rmse, o_mae, o_rmse = vali_test(model, f, f_m, o, o_m, f_o_mean_std, hyper["b_s_vt"])
print ("flow_mae", f_mae)
print ("flow_rmse", f_rmse)
print ("occ_mae", o_mae)
print ("occ_rmse", o_rmse)
return f_mae, f_rmse, o_mae, o_rmse
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
apply_to_vali_test(trained_model, vali, f_o_mean_std)
flow_mae 42.15036067679154 flow_rmse 71.01068767218318 occ_mae 0.034626120830231935 occ_rmse 0.0692719614612664
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
apply_to_vali_test(trained_model, test, f_o_mean_std)
flow_mae 40.792357901845904 flow_rmse 68.31812532539399 occ_mae 0.030586869657684364 occ_rmse 0.0622185935880154